Energy management is pursued at many levels today: in the electricity market this ranges from local energy management at industrial sites or residences, to distribution grid and transmission grid energy management; and, in the natural gas market this ranges from local energy efficiency in co-generation plants (combined heat and power production) to the gas transmission system. There is an increased need for techniques to better tune the timing and quantity of energy demand (i) to the needs of the system transmitting and distributing the energy, as well as (ii) to the needs of (instantaneously available) supply. But, the tuning of the energy demand of a load has to be done without negatively affecting the operation of the load or the processes (such as, e.g., industrial processes) relying on the operation of the load.
Regarding electricity, consider the case of a cold store at the local level (a warehouse where consumer goods such as food are stored in a frozen or chilled state). Typically, the automation of the installation is tuned to the needs of the products that are kept in the various cells of the cold store, i.e., the cold store is operated to maximize the duration of storage and minimal deterioration of the quality of the stored goods, but electricity consumption is not optimized in time with respect to electricity prices and demand-supply equilibriums in general, electricity consumption is not optimized with respect to peak power restrictions (typically, electricity consumers are penalized for excessive peaks in their power profiles), and, electricity consumption is not optimized with respect to the machines' energy efficiency (e.g., the coefficient of performance of a compressor decreases the lower the temperature). These issues are applicable to almost any industrial or residential power consumer, albeit with different specific energy efficiency problems at the machine level.
At the transmission grid level and/or at wholesale market level, consider the case of an electricity supplier or transmission system operator who is facing inefficiency problems mostly in the area of supply-demand imbalances. For the transmission system operator (TSO), these imbalances are a threat to the grid's stability—and, by its mandate, stability is a goal that must be achieved by the TSO. For the electricity supplier, unforeseen imbalances between supply and demand are typically penalized by the TSO (and, depending on the market, this could be structured in a day-ahead nomination procedure and/or an intra-day penalty market). Accordingly, not only is there a need for better forecasting in order to generate the right amount of supply, but there is also a need for flexible demand of energy, i.e., loads whose power consumption can be steered and scheduled in a fast and reliable way according to the energy players' needs—to balance out unexpected surpluses or shortages in power.
FIG. 1 illustrates an example of such an imbalance. At 9 a.m. the power production 2 greatly exceeds the demand for power 4, accordingly, an excess of power 6 is produced potentially leading to grid instability or waste. At 9:15 a.m. the demand for power 10 greatly exceeds the power production 8 leading to an excess demand 12. A similar situation is also found at 9:30 a.m. In these situations the excess supply or excess demand can lead to instability, use of inefficient resources to generate more power, and/or a potential brownout. These supply-demand imbalances indicate that energy players need flexible power control. Similarly, power consumers typically have no flexible power consumption, i.e. such power consumption cannot be tuned to the energy market needs of these energy players.
FIG. 2 illustrates a potential imbalance when a renewable source of energy such as wind is used. At 9 a.m. the combination of wind power production 22 and gas-fired power production 24 far exceeds the demand for power 26. But, at 9:15 a.m., when there is no wind, the gas production 32 is not adequate to meet the demand 34. Accordingly, FIG. 3 illustrates a prior art approach in which the gas plant is turned off and the wind production 22 is more than adequate to meet the demand 26. Of course, penalties must be paid for the residual imbalance. At 9:15 a.m., since there is no wind production, the gas-fired power production 42 is turned up to meet the demand 34 and inefficiencies result because gas-fired power production has higher variable cost and produces additional carbon emissions than relying upon wind power. In addition, relying upon an extra gas-fired production plant running at partial capacity in order to provide flexibility, results in opportunity costs and inefficiency because of heat losses due to ramping up or ramping down the plant, and lower energy efficiency of the generation itself.
At the local distribution grid level, consider an area in the distribution grid (specifically, a “terminal branch”) where there is a very high concentration of solar panels as well as a few significant industries. The energy production level will be determined by the amount of sunshine at a given time. The amount of energy demanded by the loads will be dominated by the industrial processes. Without optimization, supply and demand will be out of synchronization at this level. At times, this imbalance will cause a local oversupply of energy in the last branch of the distribution grid, which will be transported up to the higher voltage grid. This transport of energy reduces efficiency of the overall system, specifically via heat losses. Moreover, transforming the energy to a higher voltage induces strain on transformers which typically have not been designed for this. And, at times when there is not sufficient solar production to cover local demand, the energy will need to be supplied from other, farther away, less efficient energy production resources.
The above examples of imbalances have relatively large time scales, i.e., on the order of minutes rather than seconds. Indeed, on the grid level, for example, the natural time scale is typically 15 minutes since the supply-demand equilibrium for energy portfolios is defined on that scale. The same holds for typical industrial loads which again react in terms of minutes. One should note, however, that there is in addition a specific need for power which can be shifted on very short time scales. For instance, for an energy supplier with both power production and demand in its portfolio, one of the challenges is to quickly react to production plants that unexpectedly fail and stop production. This can happen on short notice, and can leave the energy supplier with an imbalance of power (demand side being larger than supply side). Ideally, the supplier would be able to switch off an amount of consumed power equal to that produced by the failing plant on a very short time scale to avoid disruptions.
Another example of the need for fast-response flexible power is given by renewable energy. As energy players have an increasing amount of wind, solar and other renewable power production in their portfolios, they are faced with unpredictable power production. For an energy supplier, the effect of an unexpected drop in wind is equivalent to a production plant outage. Here too, there is an increased need to be able to compensate on very short notice by scheduling power on the demand side of their portfolio.
There is also a need in the natural gas market for better tuning of demand. In a gas distribution system, for example, there is an increased need of balancing demand with supply. Supply in this market comes typically from shippers, who connect to terminals with storage available, and demand comes from residential and industrial natural gas consumption. While, as opposed to the electricity grid, there is significant storage availability in most gas distributions systems, there is still a need to absorb unexpected excesses of gas demand or supply, as otherwise the pressure in the gas system cannot be maintained at levels acceptable for good operation of the system. In order to provide an incentive to the shippers of natural gas active on its system, the Transmission System Operator (TSO) typically penalizes the suppliers for supply-demand imbalances. Residual imbalances on the system are then the responsibility of the TSO, and the TSO needs to have sufficient tools available—such as storage and flexible demand—to absorb these imbalances. For shippers of natural gas, it is clear that the availability of flexible demand (i.e., a set of gas loads whose demand can be steered) can help them to tune the demand to their actual gas supply, and hence avoid penalties from the TSO.
Therefore, in the overall market of energy players (both electricity and gas) and consumers (residential, commercial and industrial), there is a need for increased energy management. One specific need is for flexible power demand, i.e., control over power of a load which can be scheduled according to a need to distribute excess power or to reduce power.